Intelligent Sensing for Citizen Science

O’Grady, Michael J.; Muldoon, Conor; Carr, Dominic; Wan, Jie; Kroon, Barnard; O’Hare, Gregory M. P.

doi:10.1007/s11036-016-0682-z

Cited by 28 publications

(16 citation statements)

References 47 publications

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“…Specifically, there is an acute shortage of streamflow data in headwater catchments (Kirchner, 2006) and developing regions (Mulligan, 2013). This data gap is perpetuated by a lack of understanding among policy makers and citizens alike regarding the importance of streamflow data, which leads to persistent funding challenges (Kundzewicz, 1997;Pearson, 1998). This is further compounded by the reality that the hydrological sciences research community has focused much of its efforts in recent decades on advancing modeling techniques, while innovation in methods for generating the data these models depend on has been relegated to a lower priority (Mishra and Coulibaly, 2009;Burt and Mc-Donnell, 2015), even though these data form the foundation of hydrology (Tetzlaff et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…While the previously referenced studies focus mainly on involving citizen scientists for observing stream levels, we were primarily concerned with the possibility of enabling citizen scientists to take direct measurements of streamflow. Using keyword searches with combinations of "citizen science", "citizen hydrology", "community monitoring", "streamflow monitoring", "streamflow measurements", "smartphone streamflow measurement", and "discharge measurements", we found that research on using smartphone video processing methods for streamflow measurement has been ongoing for nearly 5 years (Lüthi et al, 2014;Peña-Haro et al, 2018). Despite the promising nature of these technologies, we could not find any specific studies evaluating the strengths and weaknesses of citizen scientists applying these technologies directly in the field themselves.…”

Section: Introductionmentioning

confidence: 99%

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Citizen science flow – an assessment of simple streamflow measurement methods

Davids

Rutten

Pandey³

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. Wise management of water resources requires data. Nevertheless, the amount of streamflow data being collected globally continues to decline. Generating hydrologic data together with citizen scientists can help fill this growing hydrological data gap. Our aim herein was to (1) perform an initial evaluation of three simple streamflow measurement methods (i.e., float, salt dilution, and Bernoulli run-up), (2) evaluate the same three methods with citizen scientists, and (3) apply the preferred method at more sites with more people. For computing errors, we used midsection measurements from an acoustic Doppler velocimeter as reference flows. First, we (authors) performed 20 evaluation measurements in headwater catchments of the Kathmandu Valley, Nepal. Reference flows ranged from 6.4 to 240 L s−1. Absolute errors averaged 23 %, 15 %, and 37 % with average biases of 8 %, 6 %, and 26 % for float, salt dilution, and Bernoulli methods, respectively. Second, we evaluated the same three methods at 15 sites in two watersheds within the Kathmandu Valley with 10 groups of citizen scientists (three to four members each) and one “expert” group (authors). At each site, each group performed three simple methods; experts also performed SonTek FlowTracker midsection reference measurements (ranging from 4.2 to 896 L s−1). For float, salt dilution, and Bernoulli methods, absolute errors averaged 41 %, 21 %, and 43 % for experts and 63 %, 28 %, and 131 % for citizen scientists, while biases averaged 41 %, 19 %, and 40 % for experts and 52 %, 7 %, and 127 % for citizen scientists, respectively. Based on these results, we selected salt dilution as the preferred method. Finally, we performed larger-scale pilot testing in week-long pre- and post-monsoon Citizen Science Flow campaigns involving 25 and 37 citizen scientists, respectively. Observed flows (n=131 pre-monsoon; n=133 post-monsoon) were distributed among the 10 headwater catchments of the Kathmandu Valley and ranged from 0.4 to 425 L s−1 and from 1.1 to 1804 L s−1 in pre- and post-monsoon, respectively. Future work should further evaluate uncertainties of citizen science salt dilution measurements, the feasibility of their application to larger regions, and the information content of additional streamflow data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Citizen science flow – an assessment of simple streamflow measurement methods

Davids

Rutten

Pandey³

et al. 2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Finally, the course attendants were asked to develop a citizen science mobile app by themselves. In this vein, smartphones enable to automate data collection and enrich observations with photographs, sound recordings and global positioning system (GPS) coordinates using embedded sensors [49]. The app requirements were: (i) to simulate the input of a numerical measurement of an external phenomenon and (ii) to compute the average of the collected measurements, excluding values out of a permitted value range.…”

Section: Methodsmentioning

confidence: 99%

Block-Based Development of Mobile Learning Experiences for the Internet of Things

Ruiz-Rube

Mota

Person

et al. 2019

Sensors

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The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.

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“…54,55 Additionally, it has been used as a framework for citizen sensing, leveraging the ubiquity of modern smartphones and wearables to provide detailed observations of remote physical locations. 56 The third evaluation focus is on code quality metrics and user evaluations. 41 The static analysis of the code base yielded favorable results when compared to that of two similar middleware offerings, ie, GSN 51 and WSNWare.…”

Section: Evaluation and Future Workmentioning

confidence: 99%

Semantic network management for next‐generation networks

Matheus¹,

Boran²,

Carr

et al. 2018

Computational Intelligence

Self Cite

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To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common data model by making intelligent inferences from low-level network and device details. Automatic discovery of new devices, monitoring of device state, and invocation of device actions in a generic fashion that is agnostic of network types is enabled. A prototype system called SNoMAC is described that employs the proposed approach operating over UPnP, TR-069, and heterogeneous sensors. These sensors are integrated by means of a sensor middleware named SIXTH that augments the capabilities of SNoMAC to allow for intelligent management and configuration of a wide variety of sensor devices. A major benefit of this approach is that the addition of new models, protocols, or sensor types merely involves the development of a new network adapter based on an ontology. Additionally, the semantic representation of the network and associated data allows for a variety of client interfaces to facilitate human input to the management and monitoring of the system.By the year 2021, the Internet of Things (IoT) is expected to encompass 3.5 times as many connected devices as there are people on Earth. 1 In addition to the sheer volume of devices, there is the added complexity of dealing with the unchecked proliferation of new network data models and protocols. To deal with these issues, network and active media applications will not only require high performance and scalability but will also need the means for quickly and dynamically evolving to accommodate the changing universe of devices. Doing this effectively necessitates a new approach for integrating network models and protocols that facilitates the intelligent management of devices across layers and at various levels of abstraction. This allows devices to be handled generically as collections while maintaining the specifics necessary to monitor and control them individually. This paper advocates an approach to solving this problem that leverages the benefits afforded by semantic web technologies, combined with the advantages of intelligent middleware. This automates much of the organization and integration of heterogeneous devices and provides a platform upon which a wide variety of applications can be built.Semantic web technologies enable the definition of formal data models called "ontologies" that provide a number of conceptual and computational benefits. This includes data model alignment, heterogeneous data integration, built-in data abstraction mechanisms, automated inferencing, dynamic meta-modeling, and automatic consistency checking. These ontologies form the base upon which semantic tools such as SPARQL, 2 SWRL, 3 and OWL, 4 or a hybrid of these, can make intelligent inferences about both devices themselves and their networ...

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Intelligent Sensing for Citizen Science

Cited by 28 publications

References 47 publications

Citizen science flow – an assessment of simple streamflow measurement methods

Citizen science flow – an assessment of simple streamflow measurement methods

Block-Based Development of Mobile Learning Experiences for the Internet of Things

Semantic network management for next‐generation networks

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